Next Article in Journal
Outlier Detection in Dynamic Systems with Multiple Operating Points and Application to Improve Industrial Flare Monitoring
Previous Article in Journal
Comparison of Polymer Networks Synthesized by Conventional Free Radical and RAFT Copolymerization Processes in Supercritical Carbon Dioxide
Previous Article in Special Issue
A Feedback Optimal Control Algorithm with Optimal Measurement Time Points
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Special Issue “Real-Time Optimization” of Processes

Laboratoire d’Automatique, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
Processes 2017, 5(2), 27;
Submission received: 24 May 2017 / Revised: 25 May 2017 / Accepted: 25 May 2017 / Published: 26 May 2017
(This article belongs to the Special Issue Real-Time Optimization)
Process optimization is the method of choice for improving the performance of industrial processes, while also enforcing the satisfaction of safety and quality constraints. Long considered an appealing tool but only applicable to academic problems, optimization has now become a viable technology. Still, one of the strengths of optimization—namely, its inherent mathematical rigor—can also be perceived as a weakness, since engineers might sometimes find it difficult to obtain an appropriate mathematical formulation to solve their practical problems. Furthermore, even when process models are available, the presence of plant–model mismatches and process disturbances makes the direct use of model-based optimal inputs hazardous. In the last 30 years, the field of real-time optimization (RTO) has emerged to help overcome the aforementioned modeling difficulties. RTO integrates process measurements into the optimization framework. This way, process optimization does not rely exclusively on a (possibly inaccurate) process model but also on process information stemming from measurements. Various RTO techniques are available in the literature and can be classified in two broad families depending on whether a process model is used (explicit optimization) or not (implicit optimization).
This special issue on real-time optimization ( includes both methodological and practical contributions [1,2,3,4,5,6,7,8,9,10,11,12,13]. All seven methodological contributions deal with explicit RTO schemes that repeat the optimization when new measurements become available. The methods covered include modifier adaptation, economic MPC, and the two-step approach of parameter identification and numerical optimization. The six contributions that deal with applications cover various fields including refineries, well networks, combustion, and membrane filtration.
This special issue shows that RTO is a very active area of research with excellent opportunities for applications. The guest editor would like to thank all authors for their timely collaboration on this project and excellent scientific contributions.


  1. Marchetti, A.; François, G.; Faulwasser, T.; Bonvin, D. Modifier Adaptation for Real-Time Optimization—Methods and Applications. Processes 2016, 4, 55. [Google Scholar] [CrossRef]
  2. Gao, W.; Hernández, R.; Engell, S. A Study of Explorative Moves during Modifier Adaptation with Quadratic Approximation. Processes 2016, 4, 45. [Google Scholar] [CrossRef]
  3. Gros, S. An Analysis of the Directional-Modifier Adaptation Algorithm Based on Optimal Experimental Design. Processes 2017, 5, 1. [Google Scholar] [CrossRef]
  4. Vaccari, M.; Pannocchia, G. A Modifier-Adaptation Strategy towards Offset-Free Economic MPC. Processes 2017, 5, 2. [Google Scholar] [CrossRef]
  5. Binette, J.; Srinivasan, B. On the Use of Nonlinear Model Predictive Control without Parameter Adaptation for Batch Processes. Processes 2016, 4, 27. [Google Scholar] [CrossRef]
  6. Suwartadi, E.; Kungurtsev, V.; Jäschke, J. Sensitivity-Based Economic NMPC with a Path-Following Approach. Processes 2017, 5, 8. [Google Scholar] [CrossRef]
  7. Jost, F.; Sager, S.; Le, T. A Feedback Optimal Control Algorithm with Optimal Measurement Time Points. Processes 2017, 5, 10. [Google Scholar] [CrossRef]
  8. Câmara, M.; Quelhas, A.; Pinto, J. Performance Evaluation of Real Industrial RTO Systems. Processes 2016, 4, 44. [Google Scholar] [CrossRef]
  9. Schäpel, J.; Reichel, T.; Klein, R.; Paschereit, C.; King, R. Online Optimization Applied to a Shockless Explosion Combustor. Processes 2016, 4, 48. [Google Scholar] [CrossRef]
  10. de Prada, C.; Sarabia, D.; Gutierrez, G.; Gomez, E.; Marmol, S.; Sola, M.; Pascual, C.; Gonzalez, R. Integration of RTO and MPC in the Hydrogen Network of a Petrol Refinery. Processes 2017, 5, 3. [Google Scholar] [CrossRef]
  11. Krishnamoorthy, D.; Foss, B.; Skogestad, S. Real-Time Optimization under Uncertainty Applied to a Gas Lifted Well Network. Processes 2016, 4, 52. [Google Scholar] [CrossRef]
  12. Jelemenský, M.; Pakšiová, D.; Paulen, R.; Latifi, A.; Fikar, M. Combined Estimation and Optimal Control of Batch Membrane Processes. Processes 2016, 4, 43. [Google Scholar] [CrossRef]
  13. Ganesh, H.; Edgar, T.; Baldea, M. Model Predictive Control of the Exit Part Temperature for an Austenitization Furnace. Processes 2016, 4, 53. [Google Scholar] [CrossRef]

Share and Cite

MDPI and ACS Style

Bonvin, D. Special Issue “Real-Time Optimization” of Processes. Processes 2017, 5, 27.

AMA Style

Bonvin D. Special Issue “Real-Time Optimization” of Processes. Processes. 2017; 5(2):27.

Chicago/Turabian Style

Bonvin, Dominique. 2017. "Special Issue “Real-Time Optimization” of Processes" Processes 5, no. 2: 27.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop